OBJECTIVES: The prevalence of overweight and obesity among children and adolescents continues to rise, becoming one of the most serious global public health issues of the 21st century. Given the differing growth and development environments between urban and rural children, associated risk factors also vary. This study aims to explore the distribution characteristics and influencing factors of overweight and obesity among urban and rural primary and secondary school students in Hunan Province, providing scientific evidence for targeted interventions. METHODS: A stratified, randomized cluster sampling method was used to select participants. A total of 197 084 students from primary and secondary schools across 14 prefectures in Hunan Province underwent physical examinations and questionnaire surveys. Population and spatial distribution characteristics of overweight and obesity were analyzed. Spatial distribution maps and spatial autocorrelation analyses were conducted using ArcGIS. Multivariate Logistic regression was used to identify influencing factors for overweight and obesity. RESULTS: The overall overweight and obesity rates among students in Hunan Province were 14.7% and 10.9%, respectively. Both rates were higher in urban areas than in rural counties (16.0% vs 13.9% for overweight; 12.1% vs 10.2% for obesity). Among both urban and rural students, boys had higher rates of overweight and obesity than girls. Higher-grade students had a higher overweight rate but a lower obesity rate than lower-grade students. In urban areas, the overweight and obesity rates of Han Chinese primary and secondary school students are lower than those of ethnic minority students (both P<0.05). In rural areas, the obesity rate of Han primary and secondary school students is lower than that of ethnic students (P<0.05). Across cities and prefectures, urban overweight and obesity rates ranged from 14.7% to 18.7% and 8.4% to 20.6% respectively, while rural rates ranged from 10.9% to 17.2% and 6.6% to 13.7% respectively. Spatial autocorrelation analysis revealed high-value clusters of overweight/obesity in urban areas of Changde and Zhangjiajie, and in rural areas of Loudi, Huaihua, and Shaoyang. Multivariate Logistic regression showed that gender, school stage, ethnicity, frequency of fresh vegetable intake, and sleep duration were associated with overweight and/or obesity in both urban and rural students. In urban students, frequency of fried food and fresh fruit intake, breakfast habits, physical activity on weekdays and holidays, and screen time on computers were also significant. In rural students, TV viewing time and sedentary duration were additional relevant factors. CONCLUSIONS The situation of overweight and obesity among primary and secondary school students in Hunan Province remains concerning. Greater attention should be paid to regions with high-value clusters of overweight/obesity, and targeted interventions should be developed based on urban-rural differences in influencing factors.
Humans ; China/epidemiology* ; Adolescent ; Male ; Female ; Rural Population/statistics & numerical data* ; Child ; Overweight/epidemiology* ; Students/statistics & numerical data* ; Urban Population/statistics & numerical data* ; Risk Factors ; Prevalence ; Obesity/epidemiology* ; Surveys and Questionnaires ; Pediatric Obesity/epidemiology* ; Schools

OBJECTIVES: The detection rate of scoliosis among school-aged children has been rising annually, varying by region, and has become a major public health concern affecting both physical and mental health. Its onset is multifactorial, and early screening combined with targeted interventions can alter disease progression. This study aims to investigate the prevalence and influencing factors of scoliosis among primary and secondary school students in Hunan Province, providing scientific evidence for targeted prevention strategies. METHODS: A stratified, randomized cluster sampling method was used to select 281 401 students from 14 prefecture-level cities in Hunan Province for scoliosis screening, physical examination, and questionnaire survey. The chi-square test was used for group comparisons, and trend chi-square test analyzed differences in screening positive rate by age. A multilevel regression model was applied to identify influencing factors, and ArcGIS was used to visualize spatial distribution patterns of scoliosis. RESULTS: The overall screening positive rate for scoliosis among Hunan students was 1.61%. Urban areas had a significantly higher rate than rural counties (2.81% vs 0.98%; P<0.01). The rate was equal between boys and girls (1.61% each). Underweight students had a higher rate than those with normal weight, overweight, or obesity (P<0.01). Stratified by age, urban students aged 6-18 years consistently showed higher positive rates than rural peers (P<0.001). No significant gender differences were observed at most ages (all P>0.05), except at age 11, where the females had a higher rate (1.28% vs 1.02%; P=0.048). After age 11, underweight students exhibited significantly higher positive rates than those with normal or higher BMI(all P<0.05). Across all groups, urban/rural, male/female, underweight/normal/overweight/obese, the scoliosis rate increased with age. By region, the screening positive rate ranged from 0.38% to 3.36%, with the top three being Chenzhou (3.36%), Xiangtan (2.78%), and Hengyang (2.71%), while the lowest was Xiangxi Tujia and Miao Autonomous Prefecture (0.38%). Multilevel regression analysis revealed that age (OR=1.160, 95% CI 1.135 to 1.186) and urban residence (OR=2.497, 95% CI 1.946 to 3.205) were positively associated with scoliosis risk (both P<0.01). Conversely, female gender (OR=0.931, 95% CI 0.874 to 0.993), normal nutritional status (OR=0.751, 95% CI 0.671 to 0.840), overweight (OR=0.513, 95% CI 0.447 to 0.590), obesity (OR=0.418, 95% CI 0.358 to 0.489), and engaging in ≥ 60 minutes of moderate-to-vigorous physical activity 2 to 4 days (OR=0.928, 95% CI 0.865 to 0.996) or 5 to 7 days per week (OR=0.912, 95% CI 0.833 to 0.998) were negatively associated with scoliosis risk (all P<0.05). CONCLUSIONS The prevalence of scoliosis among primary and secondary school students in Hunan Province is relatively high and is significantly associated with age, gender, urban-rural status, nutritional condition, and physical activity frequency. Targeted interventions and enhanced monitoring in high-risk regions and populations are essential to prevent and control scoliosis.
Humans ; Scoliosis/epidemiology* ; Male ; Female ; Adolescent ; China/epidemiology* ; Prevalence ; Child ; Students/statistics & numerical data* ; Rural Population/statistics & numerical data* ; Urban Population/statistics & numerical data* ; Surveys and Questionnaires ; Risk Factors ; Thinness/epidemiology*

Objective:To analyze the development status,frontiers and hotspots of radiomics research in the past five years from 2019 to 2023,and to provide theoretical reference and guidance for radiomics research in China.Methods:The relevant literature in the field of radiomics published in the core database of Web of Science(WOS)from January 1,2003 to August 10,2023 were searched.According to the inclusion and exclusion criteria,6,777 eligible literatures were screened and obtained,including 6,254 articles in the past five years from January 1,2019 to August 10,2023.Bibliometric methods were used to analyze the clustering of countries and regions,institutions,journals,authors,keywords and draw visual maps.Results:The 6,777 radiomics-related articles published between 2003 and 2023 were first published in 2011,and the number of papers tended to stabilize in 2018,and then the number showed a significant trend of increasing year by year.Among the 6,254 articles published from 2019 to 2023,China(3,564 articles),United States(1,164 articles),and Italy(530 articles)ranked the top 3 in terms of publication volume,with close cooperation between countries.General Electric of the United States published the most papers(448 articles),and the journal Frontiers in Oncology(704 papers)ranked first in terms of paper publication volume.From 2019 to 2023,the diseases of concern in the field of radiomics are rectal cancer,hepatocellular carcinoma,breast cancer,and lung cancer(especially non-small cell lung cancer).Conclusion:Although China ranks first in the number of national publications,the quality of research still needs to be improved.In the future,the research trend in the field of radiomics may be the diagnosis and differential diagnosis of various diseases,the prediction and evaluation of curative effect,the evaluation of tumor disease metastasis and the identification of gene phenotype based on radiomics combined with multiple imaging techniques.

Objective:To realize the optimization and upgradation of the detection method of you only look once(YOLO)algorithm model based on the improved YOLO network on the ultrasound image for breast tumor.Methods:A total of 659 images of breast tumor of the Kaggle database were selected as the initially dataset,and the image annotation tool Labelimg was used to conduct pre-labeling for the detection targets in the images.According to a ratio as 7:3,629 images of the 659 images were divided into the train set and validation set,and the other 30 images were used as the test set.The convolutional block attention module(CBAM)and bidirectional feature pyramid network(BiFPN)were introduced into the original YOLO algorithm to underwent structural improvement,which was named as YOLOv5-BiFPN-CBAM.Both the train set and validation set were placed in original YOLO algorithm model and YOLOv5-BiFPN-CBAM model to conduct train,which included 200 rounds of iterative training.The obtained optimal weight files were used in the final test of test set.Results:After 200 rounds of iterative train for two kinds of models,the test results of validation set indicated that the mean values of average precision of two kinds of models were respectively 72.1%and 80.5%for all ultrasound images of breast tumor.The result,that the optimal weight file of improved model was tested by test set,indicated the test ability of improved model was significantly enhanced than that of original model for small target in image.Conclusion:Compared with the original YOLO algorithm model,the improved YOLO algorithm model has higher recognition capability for image,which also enhances precision and sensitivity in identifying small targets of ultrasound images of breast tumor.This model is helpful to improve the diagnostic efficiency in clinical practice for breast tumor.